Method and system for reducing part missing risk and optimizing inventory for whole vehicle manufacturing enterprise by using digital tool

Abstract

The invention provides a method and a system for reducing the risk of missing parts and optimizing inventory for a finished automobile manufacturing enterprise by using a digital tool. The method comprises the following steps: S101, acquiring and transparentizing a finished automobile sequence in a production line; s103, converting the sequence of the whole vehicle into a part sequence; s105, calculating a hour-level part breakpoint according to the part sequence; and S107, through a visual interface, positioning vehicles and parts with breakpoint risks in future preset hours. According to the invention, the complexity and nonlinear relationship of data are captured, and real-time and dynamic prediction and analysis of order demands are realized; by acquiring and matching information such as production capacity, raw material supply and equipment state, combining with real-time tracking of response time of suppliers, calculating dynamic safety inventory levels at different time points, triggering hour-level alarm, determining reasonable level of safety inventory in a scientific and systematic mode, improving stability and agility of a supply chain, and improving safety and reliability of the supply chain. And the optimization of inventory management is realized.

Description

Technical Field

[0001] The present invention relates to logistics operation optimization in the field of automobile manufacturing, and more specifically, to a method and system for utilizing digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory. Background Art

[0002] Inventory management, as the core link of modern enterprise operation management, has a significant impact on logistics efficiency, operational safety and cost control. As an important indicator to ensure the continuity and stability of enterprise operations, the setting and management of safety stock is particularly important. Traditional inventory management relies on demand forecasting and manual monitoring, and is mainly managed through quantitative and qualitative methods. The commonly used inventory management form is commonly known as PPL, which uses three parameters, demand, arrival, and inventory, to predict and manage future inventory levels.

[0003] Existing methods include manually compiling a material arrival balance sheet, which adds the previous day's balance inventory to the next day's arrival inventory and subtracts the current day's consumption. If the result is positive, it means that there is no shortage of parts the next day. If the result is negative, it means that the parts cannot be met the next day and need to be replenished. The balance sheet logic is relatively simple, and some companies have adopted digital functions to achieve automatic calculations, but the minimum unit of calculation accuracy of the balance sheet logic is always days.

[0004] The disadvantage of traditional inventory management is not only that it has high requirements for data accuracy and completeness, but also that if there is a deviation in the data, it will affect the calculation results. It is also slow to respond to rapid changes in orders and cannot adjust inventory levels in time. Due to the limitations of the forecasting model, the complexity of data processing, and the interference of human factors, the traditional safety stock setting is often difficult to accurately reflect actual demand, resulting in inventory levels that are either too high, increasing storage costs, or too low, facing the risk of out-of-stock. Especially in the demand link, as young domestic consumers trigger flexible demand changes, demand can no longer be predicted using historical data, and a cliff-like decline or explosive growth may occur in a very short period of time.

[0005] The inventory data forecasting method provided by the Chinese patent "CN113449939B Inventory Data Forecasting Method, Device, Computing Equipment and Medium" is highly dependent on historical shipment and historical order volume data. If these data are missing, erroneous or biased, the forecast results may be significantly affected, resulting in inaccurate inventory forecasts. At the same time, the method assumes that historical data can well reflect future trends, but in the real world, due to the rapid changes in various factors such as market environment, consumer behavior, and supply chain conditions, historical data may not be able to fully predict the future. In addition, the determination of the target coefficient usually relies on expert experience or simple statistical analysis, which may introduce subjectivity and bias. Different target coefficients may lead to completely different forecast results, thereby affecting the accuracy of inventory decisions. The method obtains two forecast data through two independent forecasts (based on historical shipments and historical order volumes), and then simply combines them to determine the target inventory data. This simplified forecasting model may not capture the complex factors that affect inventory demand, resulting in incomplete forecast results.

[0006] Although all vehicle manufacturers and even parts suppliers have a material arrival balance sheet to determine breakpoints, its role is ultimately limited. If the inventory level is less than or close to one day, there may be a risk of connection between theoretical arrival and actual demand. For example, yesterday there were 30 sets of inventory remaining, today's demand is 50 sets, and 50 sets arrived. From the form filling, it can be seen that it is theoretically met, but if the demand occurs in the morning and the arrival occurs in the afternoon, there will be a shortage of materials or even a line stoppage. In the final analysis, when it comes to inventory management, there is currently no specific tool that can support material risk control with an accuracy of less than one day. Summary of the invention

[0007] The embodiments of the present application provide a method and system for using digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory. The method can trigger hourly alarms, determine the reasonable level of safety stock in a scientific and systematic manner, improve the stability and agility of the supply chain, and achieve optimization of inventory management.

[0008] In a first aspect, the embodiments of the present application provide a method for a vehicle manufacturer to reduce the risk of missing parts while optimizing inventory by using digital tools, including:

[0009] S101, obtain and make transparent the vehicle sequence in the production line;

[0010] S103, converting the sequence of the whole vehicle into a sequence of parts;

[0011] S105, calculating hourly part breakpoints according to the part sequence;

[0012] S107, through a visual interface, locates vehicles and parts that are at risk of breaking within the next preset hours.

[0013] Among them, step S101 includes: connecting to the production management FIS database through the data lake platform, and obtaining all vehicle passing point data of 60 points from "A700 sign printing point to Z700 final assembly point" at the same time. The vehicle passing point data includes the vehicle's unique identification information and passing time, and based on the order of the points and the vehicle passing time logic of the same point, a complete vehicle passing point sequence is spliced. The method used for sequence splicing is: each workshop is arranged in the order of first-in-first-out, and then spliced ​​into a total sequence. Adjustments will be made based on actual conditions when promoting across factories.

[0014] Among them, step S103 includes: after obtaining the whole vehicle sequence, decomposing the whole vehicle sequence into hourly parts demand; obtaining the whole vehicle to part decomposition relationship; in the data lake platform, integrating the hourly parts demand data and the whole vehicle to part decomposition relationship to obtain the parts hourly point sequence wide table.

[0015] Wherein, step S103 also includes: using a planning data table as data support for all parts loading points.

[0016] Among them, step S105 includes: obtaining hourly WMS inventory data, WMS inventory types include regular warehouse inventory, claim warehouse, and frozen warehouse. The calculation of breakpoints requires range screening and definition, and aggregation of the same part in different warehouses based on part numbers; since the loading quantity of each part is different, in the process of calculating inventory deduction requirements, the loading quantity of each part in the BOM needs to be integrated and calculated together; after obtaining the data of the above two sections, the wide table of part sequence breakpoints will be integrated as the basis for the final calculation of breakpoint time.

[0017] Among them, in step S105, the breakpoint calculation logic of the part is as follows:

[0018] Location of breakpoint vehicle: the inventory quantity of the part < the serial number of the vehicle using the part

[0019] Breakpoint time warning calculation: full vehicle serial number corresponding to the breakpoint vehicle / 60JPH.

[0020] Among them, step S107 includes: at the vehicle level, locating vehicles with breakpoint risks in the next 24 hours and the current passing point positions of the vehicles; at the parts level: locating the corresponding parts with breakpoint risks in the next 24 hours, and how long it is expected that each part will break, and the number of parts required for emergency pulling.

[0021] On the second aspect, the present application provides a system that uses digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory. The system is configured to: obtain and make transparent the sequence of complete vehicles within the production line; convert the sequence of complete vehicles into a parts sequence; calculate the hourly parts breakpoints based on the parts sequence; and locate vehicles and parts that are at risk of breakpoints within a preset hour in the future through a visual interface.

[0022] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0023] In a fourth aspect, an embodiment of the present application provides a computer system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the program.

[0024] The method and system of the embodiment of the present application using digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory have the following beneficial effects:

[0025] This application is the first in the vehicle manufacturing industry to use digital tools to find dynamic safety stocks of thousands of parts at the same time, laying a new theoretical foundation for inventory optimization. At the same time, this application integrates orders and real vehicle demand on the assembly line, with a calculation frequency of hours, making it an efficient tool that can accurately determine the risk of material breakpoints. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a method for using digital tools to reduce the risk of missing parts and optimize inventory for vehicle manufacturers in an embodiment of the present application;

[0027] Figure 2 A sequence simulation diagram in this application;

[0028] Figure 3 This is the system logic diagram of this application;

[0029] Figure 4 This is the splicing logic diagram in this application;

[0030] Figure 5 This is a logical diagram of the decomposition of the entire vehicle into parts in this application;

[0031] Figure 6 This is a schematic diagram of the visualization interface in this application. DETAILED DESCRIPTION

[0032] The present application is further described below in conjunction with the accompanying drawings and embodiments.

[0033] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present invention, and different embodiments can be replaced or combined, so this application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more of all other possible combinations of features A, B, C, and D, even though the embodiment may not be clearly recorded in the following text.

[0034] like Figure 1 As shown, the present application utilizes digital tools to enable vehicle manufacturers to reduce the risk of parts shortages while optimizing inventory, including: S101, obtaining and making transparent the sequence of vehicles in the production line; S103, converting the sequence of vehicles into a parts sequence; S105, calculating hourly parts breakpoints based on the parts sequence; S107, locating vehicles and parts with breakpoint risks within a preset hour in the future through a visual interface.

[0035] This application takes vehicle manufacturing enterprises as the research object and proposes an innovative digital dynamic safety stock solution. This solution aims to capture the complexity and nonlinear relationship of data through time series, online distribution and vehicle configuration information by introducing advanced data analysis technology and algorithm models, so as to achieve real-time and dynamic prediction and analysis of order demand. In addition, by acquiring and matching information such as production capacity, raw material supply, equipment status, etc., combined with real-time tracking of supplier response time, the dynamic safety stock level at different time points is calculated, triggering hourly alarms, and determining the reasonable level of safety stock in a scientific and systematic way, improving the stability and agility of the supply chain, and optimizing inventory management. The following is a detailed introduction.

[0036] For this solution, we carried out a long period of preparation in the early stage, mainly to test several key links. First, how to produce the sequence and whether the accuracy is reliable; second, how to obtain the consumption of bicycle parts and whether the accuracy is reliable; third, how to operate the system and whether it is compatible.

[0037] Key Step 1: Evaluation of the Stability of the Factory Vehicle Production Sequence

[0038] In order to convert the entire vehicle from the body to the final assembly line on the production line into hourly parts demand, it is first necessary to piece together a relatively stable vehicle sequence within the refresh time interval. Only in this way can the parts demand decomposed from the sequence have warning value for the next 24 hours. This is the basis of the entire project.

[0039] like Figure 2As shown, first, the project team evaluated the sequence splicing method. The process of the standardized factory will go through the following workshops: stamping workshop, body workshop, paint workshop and assembly workshop. Yizheng is a standardized factory, and there is also a BDC warehouse between the paint and assembly workshops. The purpose is to control the order of the workshops as much as possible. Therefore, according to the above statement, there will be at least three vehicle sequence orders for the entire assembly line.

[0040] The first one: each workshop is arranged in the order of first-in-first-out, and then spliced ​​into a total sequence.

[0041] The second method: each workshop is first arranged according to the sequence restored by A500, and then spliced ​​into the overall sequence.

[0042] The third method: each workshop is arranged in the order of first-in, first-out, then BDC, and the final assembly workshop is assembled into a total sequence according to the restoration result of A500.

[0043] During the testing phase, the time data of each production point can be downloaded from the production system FIS, and a complete vehicle sequence can be spliced ​​based on the order of production points and the time of each vehicle in the unified point. After multiple versions of data testing, the sequence restoration degree of Scheme 1 reached more than 80%, which is the highest among the three schemes. Combined with the actual operation of the BDC in the current factory, Scheme 1 was finally selected as the sequence simulation scheme for the Yizheng factory.

[0044] Key Step 2: Overall System Blueprint Design

[0045] After clarifying the project premise, the logistics, logistics system planning and IT departments of Yizheng Factory began to design and improve the system development blueprint. The overall logic of the blueprint is as follows: Figure 3 As shown, the main steps are:

[0046] 1. Obtain and make transparent the vehicle sequence within the production line

[0047] like Figure 4 As shown in the figure, the production management FIS database is connected through the data lake, and at the same time, all vehicle passing point data of 60 points from "A700 sign printing point to Z700 final assembly point" are obtained, including vehicle unique identification information and passing point time, etc., and based on the order of the points and the vehicle passing point time logic of the same point, a complete vehicle passing point sequence is spliced.

[0048] 2. Convert the vehicle sequence into a parts sequence

[0049] like Figure 5As shown in the figure, after obtaining the vehicle sequence, the next step is to decompose it into hourly parts demand. Each vehicle has about 1,500 parts, and the number of vehicles in the vehicle sequence at the same time is about 2,000. The amount of calculation is extremely large. In this regard, the project team decided to run the decomposition calculation process in the data lake. The main logic is: vehicle sequence + vehicle to parts decomposition relationship table → parts sequence table.

[0050] The next step is to obtain the decomposition relationship from the whole vehicle to the parts. The JIS / JIT OPCS pulling system has been put into use. This system realizes the point-triggered pulling when the JIT and JIS parts are in short supply to the hourly level. Of course, it does not cover long-distance JIT and LAH parts. However, during the development process, the system integrated the key data of order systems such as TGN and Infas and parts technical systems. The result table of the decomposition of vehicles produced in the past two days to parts is calculated once a day. After data verification and checking, the project team believes that this result table can be integrated through the data lake and directly reused in this project.

[0051] Finally, the data lake will integrate the above two data sources to form a wide table of hourly passing sequence of parts. It should be noted that since the breakpoint calculation starts from the loading point, the closer to the loading point, the smaller the serial number of the vehicle and parts should be, so a planning data table is needed as the data support for all parts loading points, otherwise it is impossible to locate the loading part corresponding to serial number 1 for each part, and the subsequent breakpoint calculation will be inaccurate.

[0052] 3. Calculation of part breakpoints

[0053] After calculating the parts passing sequence table, the next step is to calculate the breakpoint. First, you need to obtain hourly WMS inventory data. There are many types of WMS inventory, including regular warehouse inventory, claim warehouse, frozen warehouse, etc. The calculation of the breakpoint requires range screening and definition, and the same part in different warehouses is aggregated based on the part number to ensure the completeness and accuracy of the inventory data. In addition, since the loading quantity of each part is different, in the process of calculating the inventory deduction demand, the loading number of each part in the BOM needs to be integrated and calculated together. After obtaining the data of the above two sections, the wide table of part sequence breakpoints will be integrated as the basis for the final calculation of the breakpoint time. It should be noted that since there are two situations of vehicle loading parts, mandatory and optional, but optional parts only mean that the number of loadings in the front is less, so the inventory can be less, but the waiting time in the breakpoint calculation process will still be included, so the total sequence and loading sequence of the parts need to be divided in the wide table. The latter is used for the sequence positioning of the breakpoint vehicle, and the former is used for the calculation of the breakpoint time. The breakpoint calculation logic example of a part is as follows:

[0054] Location of breakpoint vehicle: the inventory quantity of the part < the serial number of the vehicle using the part

[0055] Breakpoint time warning calculation: full vehicle serial number corresponding to the breakpoint vehicle / 60JPH, JPH (Jobs Per Hour) is the hourly workload or the workload per unit time.

[0056] Key Step 3: Evaluate the availability of relevant system data sources

[0057] The present invention involves three factory-owned systems, namely the OPCS system, which is responsible for the SAIC Volkswagen JIS / JIT parts pulling system; the WMS system, which is responsible for the receipt and delivery of SAIC Volkswagen parts inventory; and the FIS system, which is responsible for the vehicle scheduling and control at the manufacturing end. Each system needs to be tested separately to test whether the data can be captured, whether the captured form can be used, and the validity of the data. For example, the OPCS system, only this system can clearly decompose what parts are installed on each vehicle, but is the data complete? Whether it can be exported directly and whether the exported format can be directly used was not known at first. The team actively contacted the system operation and maintenance service provider to analyze them one by one.

[0058] For example, the WMS system is responsible for the inventory statistics of SAIC Volkswagen parts. But are the results of the system query consistent with the exported results? Whether there is a delay in the system or whether the data is biased cannot be determined. Therefore, the team spent a lot of effort to check the differences and find the correct statistical method. Similarly, the FIS system is closely related to the sequence. The original sequence and the sequence we want to process need to be manually selected and reorganized. In this process, we will encounter the problem of order retention and the interference of batch and non-batch orders.

[0059] After multiple manual data verifications and evaluations with the department responsible for system applications, it was confirmed that the three systems can support direct connection to the data lake for data extraction every two hours. The refresh frequency of the source data of each system is as follows, which can meet the data refresh needs of this project:

[0060] system Key data Refresh frequency OPCS Vehicle-parts decomposition results every day WMS Parts Inventory Data real time FIS Vehicle passing data real time PBOM Number of parts loaded every day

[0061] Key Step 4: Hourly refresh support for systems, databases, and reporting centers

[0062] Report and database platform: After evaluating all related data, the data obtained by different systems need to be placed on a platform for overall operation, which requires the platform to have coordination and operation capabilities. Without a suitable platform, it is also impossible to meet the needs of big data operations. Thanks to SAIC Volkswagen's new data lake platform as the overall data center and the BI report platform as the report center, it has been evaluated by the IT and logistics system planning departments, integrating the output results of multiple systems and can support the current 2-hour frequency operation conditions.

[0063] Key Step 5: Digital System Development

[0064] The business functions of the system are implemented on the company's big data platform, from data collection, business calculations to visual display, all built on the data lake platform. The entire system function implementation involves long data links and many technical components, such as Kafka, Hive, Airflow, PowerBI, etc., which are indispensable in data synchronization, analysis and calculation, task scheduling and monitoring, and report presentation.

[0065] like Figure 6 As shown, the report development is completed through the BI report tool, and the visual interface can be used directly: Vehicle level: quickly locate the vehicles with breakpoint risks in the next 24 hours and the current crossing point positions of the vehicles. Parts level: quickly locate the corresponding parts with breakpoint risks in the next 24 hours, and how long each part is expected to break, how many parts are needed for emergency pull, the corresponding planners, the way parts enter the factory and other key information.

[0066] This application is the first in the vehicle manufacturing industry to use digital tools to find the dynamic safety inventory of thousands of parts at the same time, laying a new theoretical foundation for inventory optimization. At the same time, this application integrates orders and real vehicle demand on the assembly line, and uses a 2-hour calculation frequency to accurately determine the risk of material breakpoints.

[0067] This application has the following effects:

[0068] (1) The inventory management optimization and monitoring tools are independently developed and innovated, without any development costs, achieving zero-cost digital innovation.

[0069] (2) Through risk control of expected parts, the feasibility of reducing the overall inventory period was achieved, and the warehouse inventory funds were successfully reduced by 20 million, and the warehouse storage area was optimized and released by 3,000m 2 , providing the necessary area for the relocation of the Passat B8 / B9 project to Yizheng in early 2025. Otherwise, 3000m 2 The need to borrow a venue requires an additional cost of approximately RMB 1.64 million per year.

[0070] (3) This application has won the SAIC Volkswagen Internal Case Innovation Award. If the model is promoted to other factories of Volkswagen or even SAIC Group, the results will be even greater. Currently, the Anting Electric Vehicle Factory is also being tested to see if it can be successfully grafted with this optimization project.

[0071] (4) The 2024 research topic of the China Federation of Logistics and Purchasing has great promotion value for vehicle manufacturers. The inventory management idea is at the leading level in the current industry, and this innovative research idea deserves to be protected.

[0072] The present application also provides a system that uses digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory. The system is configured to: obtain and make transparent the sequence of complete vehicles within the production line; convert the sequence of complete vehicles into a parts sequence; calculate the hourly parts breakpoints based on the parts sequence; and locate vehicles and parts that are at risk of breakpoints within a preset number of hours in the future through a visual interface.

[0073] In the present application, the system implementation example of using digital tools to enable complete vehicle manufacturers to reduce the risk of missing parts while optimizing inventory is basically similar to the method implementation example of using digital tools to enable complete vehicle manufacturers to reduce the risk of missing parts while optimizing inventory. For relevant matters, please refer to the introduction of the method implementation example of using digital tools to enable complete vehicle manufacturers to reduce the risk of missing parts while optimizing inventory.

[0074] The present application also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method steps for using digital tools to enable vehicle manufacturers to reduce the risk of missing parts while optimizing inventory are implemented.

[0075] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method steps for using digital tools to reduce the risk of missing parts and optimize inventory for vehicle manufacturers are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, micro drives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nano systems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers using digital tools, characterized in that: include: S101, obtain and make transparent the vehicle sequence in the production line; S103, converting the sequence of the whole vehicle into a sequence of parts; S105, calculating hourly part breakpoints according to the part sequence; S107, through a visual interface, locates vehicles and parts that are at risk of breaking within the next preset hours.

2. The method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers by using digital tools according to claim 1 is characterized in that: Step S101 includes: connecting to the production management FIS database through the data lake platform, and obtaining all vehicle passing point data of 60 points from "A700 sign printing point to Z700 final assembly point" at the same time. The vehicle passing point data includes the vehicle's unique identification information and passing point time, and based on the order of the points and the vehicle passing point time logic of the same point, a complete vehicle passing point sequence is spliced. The method used for sequence splicing is: each workshop is first arranged in the order of first-in-first-out, and then spliced ​​into a total sequence. Adjustments will be made based on actual conditions when promoting across factories.

3. The method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers by using digital tools according to claim 1 or 2, characterized in that: Step S103 includes: after obtaining the whole vehicle sequence, decomposing the whole vehicle sequence into hourly parts demand; obtaining the whole vehicle to parts decomposition relationship; integrating the hourly parts demand data and the whole vehicle to parts decomposition relationship in the data lake platform to obtain a wide table of hourly parts transit sequence.

4. The method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers by using digital tools according to claim 3 is characterized in that: Step S103 also includes: using a planning data table as data support for all parts loading points.

5. The method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers by using digital tools according to claim 1 or 2, characterized in that: Step S105 includes: obtaining hourly WMS inventory data. WMS inventory types include regular warehouse inventory, claim warehouse, and frozen warehouse. The calculation of breakpoints requires range screening and definition, and aggregation of the same part in different warehouses based on part numbers. Since the loading quantity of each part is different, the loading quantity of each part in the BOM needs to be integrated and calculated together in the process of calculating inventory deduction requirements. After obtaining the data of the above two sections, the wide table of part sequence breakpoints will be integrated as the basis for the final calculation of breakpoint time.

6. The method for reducing the risk of missing parts and optimizing inventory for vehicle manufacturers using digital tools according to claim 5 is characterized in that: In step S105, the breakpoint calculation logic of the part is as follows: Location of breakpoint vehicle: the inventory quantity of the part < the serial number of the vehicle using the part Breakpoint time warning calculation: full vehicle serial number corresponding to the breakpoint vehicle / 60JPH.

7. The method for using digital tools to reduce the risk of missing parts and optimize inventory for vehicle manufacturers according to claim 1 or 2, characterized in that: Step S107 includes: at the vehicle level, locating vehicles with breakpoint risks in the next 24 hours and the current breakpoint positions of the vehicles; at the parts level: locating the corresponding parts with breakpoint risks in the next 24 hours, and how long it is expected that each part will break, and the number of parts required for emergency pulling.

8. A system that uses digital tools to reduce the risk of missing parts and optimize inventory for vehicle manufacturers, characterized in that: The system is configured to: obtain and make transparent the complete vehicle sequence within the production line; convert the complete vehicle sequence into a parts sequence; calculate the hourly parts breakpoints based on the parts sequence; and locate vehicles and parts that are at risk of breakpoints within a preset hour in the future through a visual interface.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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